source: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SymbolicTimeSeriesPrognosisModel.cs @ 8458

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
  /// <summary>
  /// Represents a symbolic time-series prognosis model
  /// </summary>
  [StorableClass]
  [Item(Name = "Symbolic Time-Series Prognosis Model", Description = "Represents a symbolic time series prognosis model.")]
  public class SymbolicTimeSeriesPrognosisModel : SymbolicRegressionModel, ISymbolicTimeSeriesPrognosisModel {

    public new ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter Interpreter {
      get { return (ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter)base.Interpreter; }
    }

    [StorableConstructor]
    protected SymbolicTimeSeriesPrognosisModel(bool deserializing) : base(deserializing) { }
    protected SymbolicTimeSeriesPrognosisModel(SymbolicTimeSeriesPrognosisModel original, Cloner cloner) : base(original, cloner) { }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new SymbolicTimeSeriesPrognosisModel(this, cloner);
    }

    public SymbolicTimeSeriesPrognosisModel(ISymbolicExpressionTree tree, ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, double lowerLimit = double.MinValue, double upperLimit = double.MaxValue) : base(tree, interpreter, lowerLimit, upperLimit) { }



    public IEnumerable<IEnumerable<double>> GetPrognosedValues(Dataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
      var estimatedValues = Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows, horizons);
      return estimatedValues.Select(predictionPerRow => predictionPerRow.LimitToRange(LowerEstimationLimit, UpperEstimationLimit));
    }

    public ISymbolicTimeSeriesPrognosisSolution CreateTimeSeriesPrognosisSolution(ITimeSeriesPrognosisProblemData problemData) {
      return new SymbolicTimeSeriesPrognosisSolution(this, problemData);
    }
    ITimeSeriesPrognosisSolution ITimeSeriesPrognosisModel.CreateTimeSeriesPrognosisSolution(ITimeSeriesPrognosisProblemData problemData) {
      return CreateTimeSeriesPrognosisSolution(problemData);
    }

    //public static void Scale(SymbolicTimeSeriesPrognosisModel model, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
    //  var dataset = problemData.Dataset;
    //  var targetVariable = problemData.TargetVariable;
    //  var estimatedValues = model.Interpreter.GetSymbolicExpressionTreeValues(model.SymbolicExpressionTree, dataset, rows);
    //  var boundedEstimatedValues = estimatedValues.LimitToRange(model.lowerEstimationLimit, model.upperEstimationLimit);
    //  var targetValues = problemData.Dataset.GetDoubleValues(targetVariable, rows);

    //  double alpha, beta;
    //  OnlineCalculatorError error;
    //  OnlineLinearScalingParameterCalculator.Calculate(boundedEstimatedValues, targetValues, out alpha, out beta, out error);
    //  if (error != OnlineCalculatorError.None) return;

    //  ConstantTreeNode alphaTreeNode = null;
    //  ConstantTreeNode betaTreeNode = null;
    //  // check if model has been scaled previously by analyzing the structure of the tree
    //  var startNode = model.SymbolicExpressionTree.Root.GetSubtree(0);
    //  if (startNode.GetSubtree(0).Symbol is Addition) {
    //    var addNode = startNode.GetSubtree(0);
    //    if (addNode.SubtreeCount == 2 && addNode.GetSubtree(0).Symbol is Multiplication && addNode.GetSubtree(1).Symbol is Constant) {
    //      alphaTreeNode = addNode.GetSubtree(1) as ConstantTreeNode;
    //      var mulNode = addNode.GetSubtree(0);
    //      if (mulNode.SubtreeCount == 2 && mulNode.GetSubtree(1).Symbol is Constant) {
    //        betaTreeNode = mulNode.GetSubtree(1) as ConstantTreeNode;
    //      }
    //    }
    //  }
    //  // if tree structure matches the structure necessary for linear scaling then reuse the existing tree nodes
    //  if (alphaTreeNode != null && betaTreeNode != null) {
    //    betaTreeNode.Value *= beta;
    //    alphaTreeNode.Value *= beta;
    //    alphaTreeNode.Value += alpha;
    //  } else {
    //    var mainBranch = startNode.GetSubtree(0);
    //    startNode.RemoveSubtree(0);
    //    var scaledMainBranch = MakeSum(MakeProduct(mainBranch, beta), alpha);
    //    startNode.AddSubtree(scaledMainBranch);
    //  }
    //}

    //private static ISymbolicExpressionTreeNode MakeSum(ISymbolicExpressionTreeNode treeNode, double alpha) {
    //  if (alpha.IsAlmost(0.0)) {
    //    return treeNode;
    //  } else {
    //    var addition = new Addition();
    //    var node = addition.CreateTreeNode();
    //    var alphaConst = MakeConstant(alpha);
    //    node.AddSubtree(treeNode);
    //    node.AddSubtree(alphaConst);
    //    return node;
    //  }
    //}

    //private static ISymbolicExpressionTreeNode MakeProduct(ISymbolicExpressionTreeNode treeNode, double beta) {
    //  if (beta.IsAlmost(1.0)) {
    //    return treeNode;
    //  } else {
    //    var multipliciation = new Multiplication();
    //    var node = multipliciation.CreateTreeNode();
    //    var betaConst = MakeConstant(beta);
    //    node.AddSubtree(treeNode);
    //    node.AddSubtree(betaConst);
    //    return node;
    //  }
    //}

    //private static ISymbolicExpressionTreeNode MakeConstant(double c) {
    //  var node = (ConstantTreeNode)(new Constant()).CreateTreeNode();
    //  node.Value = c;
    //  return node;
    //}
  }
}